Pandemics and socio-economic status. Evidence from the plague of 1630 in northern Italy.

This paper investigates the biological, socio-economic, and institutional factors shaping the individual risk of death during a major pre-industrial epidemic. We use a micro-demographic database for an Italian city (Carmagnola) during the 1630 plague to explore in detail the survival dynamics of the population admitted to the isolation hospital (lazzaretto). We develop a theoretical model of admissions to the lazzaretto, for better interpretation of the observational data. We explore how age and sex shaped the individual risk of death, and we provide a one-of-a-kind study of the impact of socio-economic status. We report an inversion of the normal mortality gradient by status for those interned at the lazzaretto. The rich enjoyed a greater ability to make decisions about their hospitalization, but this backfired. Instead, the poor sent to the lazzaretto faced a relatively low risk of death because they enjoyed better conditions than they would have experienced outside the hospital.

Approximate Bayesian computation for the natural history of breast cancer, with application to data from a Milan cohort study.

We explore models for the natural history of breast cancer, where the main events of interest are the start of asymptomatic detectability of the disease (through screening) and the time of symptomatic detection (through symptoms). We develop several parametric specifications based on a cure rate structure, and present the results of the analysis of data collected as part of a motivating study from Milan. Participants in the study were part of a regional breast cancer screening program, and their ten-year trajectories were obtained from administrative data available from the Italian national health care system. We first present a tractable model for which we develop the likelihood contributions of the observed trajectories and perform maximum likelihood inference on the latent process. Likelihood based inference is not feasible for more flexible models, and we implement approximate Bayesian computation (ABC) for inference. Issues that arise from the use of ABC for model choice and parameter estimation are discussed, including the problem of choosing appropriate summary statistics. The estimated parameters of the underlying disease process allow for the study of the effect of different examination schedules (age range and frequency of screening examinations) on a population of asymptomatic subjects.

Evaluation of age-specific causes of death in the context of the Italian longevity transition.

In many low-mortality countries, life expectancy at birth increased steadily over the last century. In particular, both Italian females and males benefited from faster improvements in mortality compared to other high-income countries, especially from the 1960s, leading to an exceptional increase in life expectancy. However, Italy has not become the leader in longevity. Here, we investigate life expectancy trends in Italy during the period 1960-2015 for both sexes. Additionally, we contribute to the existing literature by complementing life expectancy with an indicator of dispersion in ages at death, also known as lifespan inequality. Lifespan inequality underlies heterogeneity over age in populating health improvements and is a marker of uncertainty in the timing of death. We further quantify the contributions of different age groups and causes of death to recent trends in life expectancy and lifespan inequality. Our findings highlight the contributions of cardiovascular diseases and neoplasms to the recent increase in life expectancy but not necessarily to the decrease in lifespan inequality. Our results also uncover a more recent challenge across Italy: worsening mortality from infectious diseases and mortality at older age.

Freedom of Choice to Vaccinate and COVID-19 Vaccine Hesitancy in Italy.

Despite the availability of effective vaccines that lower mortality and morbidity associated with COVID-19, many countries including Italy have adopted strict vaccination policies and mandates to increase the uptake of the COVID-19 vaccine. Such mandates have sparked debates on the freedom to choose whether or not to get vaccinated. In this study, we examined the people's belief in vaccine choice as a predictor of willingness to get vaccinated among a sample of unvaccinated individuals in Italy. An online cross-sectional survey was conducted in Italy in May 2021. The survey collected data on respondents' demographics and region of residence, socioeconomic factors, belief in the freedom to choose to be vaccinated or not, risk perception of contracting and transmitting the disease, previous vaccine refusal, opinion on adequacy of government measures to address the pandemic, experience in requesting and being denied government aid during the pandemic, and intent to accept COVID-19 vaccination. The analysis employed binary logistic regression models using a hierarchical model building approach to assess the association between intent to accept vaccination and belief in the freedom to choose to vaccinate, while adjusting for other variables of interest. 984 unvaccinated individuals were included in the study. Respondents who agreed that people should be free to decide whether or not to vaccinate with no restrictions on their personal life had 85% lower odds of vaccine acceptance (OR = 0.15; 95% CI, 0.09,0.23) after adjusting for demographic and socioeconomic factors and their risk perception of contracting and transmitting COVID-19. Belief in the freedom to choose whether or not to accept vaccinations was a major predictor of COVID-19 vaccine acceptance among a sample of unvaccinated individuals in Italy in May 2021. This understanding of how individuals prioritize personal freedoms and the perceived benefits and risks of vaccines, when making health care decisions can inform the development of public health outreach, educational programs, and messaging.

Cross-National Vaccine Concerns and Predictors of Vaccine Hesitancy in Not-Fully Vaccinated Individuals: Findings from USA, Canada, Sweden, and Italy.

Vaccine hesitancy is a key contributor to reduced COVID-19 vaccine uptake and remains a threat to COVID-19 mitigation strategies as many countries are rolling out the campaign for booster shots. The goal of our study is to identify and compare the top vaccine concerns in four countries: Canada, Italy, Sweden, and the USA and how these concerns relate to vaccine hesitancy. While most individuals in these countries are now vaccinated, we expect our results to be helpful in guiding vaccination efforts for additional doses, and more in general for other vaccines in the future. We sought to empirically test whether vaccine related concerns followed similar thematic issues in the four countries included in this study, and then to see how these themes related to vaccine hesitancy using data from a cross-sectional survey conducted in May 2021. We applied CFA and created vaccine concern scales for analysis. We then utilized these results in regression-based modeling to determine how concerns related to vaccine hesitancy and whether there were similar or different concerns by country. The results quantitatively highlight that the same vaccine related concerns permeated multiple countries at the same point in time. This implies that COVID-19 vaccination communications could benefit from global collaboration.

Exploring the Association between Misinformation Endorsement, Opinions on the Government Response, Risk Perception, and COVID-19 Vaccine Hesitancy in the US, Canada, and Italy.

The COVID-19 pandemic has highlighted the adverse consequences created by an infodemic, specifically bringing attention to compliance with public health guidance and vaccine uptake. COVID-19 vaccine hesitancy is a complex construct that is related to health beliefs, misinformation exposure, and perceptions of governmental institutions. This study draws on theoretical models and current data on the COVID-19 infodemic to explore the association between the perceived risk of COVID-19, level of misinformation endorsement, and opinions about the government response on vaccine uptake. We surveyed a sample of 2697 respondents from the US, Canada, and Italy using a mobile platform between 21-28 May 2021. Using multivariate regression, we found that country of residence, risk perception of contracting and spreading COVID-19, perception of government response and transparency, and misinformation endorsement were associated with the odds of vaccine hesitancy. Higher perceived risk was associated with lower odds of hesitancy, while lower perceptions of government response and higher misinformation endorsement were associated with higher hesitancy.

COVID-19 Vaccine Concerns about Safety, Effectiveness, and Policies in the United States, Canada, Sweden, and Italy among Unvaccinated Individuals.

Despite the effectiveness of the COVID-19 vaccine, global vaccination distribution efforts have thus far had varying levels of success. Vaccine hesitancy remains a threat to vaccine uptake. This study has four objectives: (1) describe and compare vaccine hesitancy proportions by country; (2) categorize vaccine-related concerns; (3) rank vaccine-related concerns; and (4) compare vaccine-related concerns by country and hesitancy status in four countries-the United States, Canada, Sweden, and Italy. Using the Pollfish survey platform, we sampled 1000 respondents in Canada, Sweden, and Italy and 750 respondents in the United States between 21-28 May 2021. Results showed vaccine-related concerns varied across three topical areas-vaccine safety and government control, vaccine effectiveness and population control, and freedom. For each thematic area, the top concern was statistically significantly different in each country and among the hesitant and non-hesitant subsamples within each county. Concerns related to freedom were the most universal. Understanding the specific concerns among individuals when it comes to the COVID-19 vaccine can help to inform public communications and identify which, if any, salient narratives are global.

Linking excess mortality to mobility data during the first wave of COVID-19 in England and Wales.

Non-pharmaceutical interventions have been implemented worldwide to curb the spread of COVID-19. However, the effectiveness of such governmental measures in reducing the mortality burden remains a key question of scientific interest and public debate. In this study, we leverage digital mobility data to assess the effects of reduced human mobility on excess mortality, focusing on regional data in England and Wales between February and August 2020. We estimate a robust association between mobility reductions and lower excess mortality, after adjusting for time trends and regional differences in a mixed-effects regression framework and considering a five-week lag between the two measures. We predict that, in the absence of mobility reductions, the number of excess deaths could have more than doubled in England and Wales during this period, especially in the London area. The study is one of the first attempts to quantify the effects of mobility reductions on excess mortality during the COVID-19 pandemic.

Computing the exact distributions of some functions of the ordered multinomial counts: maximum, minimum, range and sums of order statistics.

Starting from seminal neglected work by Rappeport (Rappeport 1968 Algorithms and computational procedures for the application of order statistics to queuing problems. PhD thesis, New York University), we revisit and expand on the exact algorithms to compute the distribution of the maximum, the minimum, the range and the sum of the J largest order statistics of a multinomial random vector under the hypothesis of equiprobability. Our exact results can be useful in all those situations in which the multinomial distribution plays an important role, from goodness-of-fit tests to the study of Poisson processes, with applications spanning from biostatistics to finance. We describe the algorithms, motivate their use in statistical testing and illustrate two applications. We also provide the codes and ready-to-use tables of critical values.

Isochronal superpositioning in the equilibrium regime of superpressed propylene carbonate to ∼ 1.8 GPa: A study by diffusivity measurement of the fluorescent probe Coumarin 1.

We address the problem of glass-forming of liquids by superpressing. We study the pressure-induced dynamic change of the fragile van der Waals liquid propylene carbonate towards the glassy state in the equilibrium regime by measuring the diffusivity of the fluorescent probe Coumarin 1 embedded in the host liquid. The probe diffusivity is measured by the fluorescence recovery after photobleaching (FRAP) technique across a bleached volume generated by the near-field diffracted pattern of a laser beam. The recovered fluorescence intensity fits to a stretched exponential with the diffusive time [Formula: see text] and the stretched exponent [Formula: see text] as free parameters. In the pressure range [0.3-1.0]GPa the diffusivity decouples from the Stokes-Einstein relation. The decoupling correlates well to a decrease of [Formula: see text]. The variation of [Formula: see text] is non-monotonous with [Formula: see text] showing a minimum at [Formula: see text] s. We evidence an isochronal superpositioning over about 3 decades of [Formula: see text] between ∼ 10 s and [Formula: see text] s and a density scaling in the whole investigated pressure range. The pressure at which [Formula: see text] is minimum coincides to the dynamical crossover pressure measured by other authors. This crossover pressure is compatible with the critical point of MCT theory. As our studied pressure range encompasses the critical pressure, the non-monotonous variation of [Formula: see text] opens new insight in the approach to the critical point.

Neurocognitive assessment in obsessive compulsive disorder patients: Adherence to behavioral decision models.

In economics, models of decision-making under risk are widely investigated. Since many empirical studies have shown patterns in choice behavior that classical models fail to predict, several descriptive theories have been developed. Due to an evident phenotypic heterogeneity, obsessive compulsive disorder (OCD) patients have shown a general deficit in decision making when compared to healthy control subjects (HCs). However, the direction for impairment in decision-making in OCD patients is still unclear. Hence, bridging decision-making models widely used in the economic literature with mental health research may improve the understanding of preference relations in severe patients, and may enhance intervention designs. We investigate the behavior of OCD patients with respect to HCs by means of decision making economic models within a typical neuropsychological setting, such as the Cambridge Gambling Task. In this task subjects have to decide the amount of their initial wealth to invest in each risky decision. To account for heterogenous preferences, we have analyzed the micro-level data for a more informative analysis of the choices made by the subjects. We consider two influential models in economics: the expected value (EV), which assumes risk neutrality, and a multiple reference points model, an alternative formulation of Disappointment theory. We find evidence that (medicated) OCD patients are more consistent with EV than HCs. The former appear to be more risk neutral, namely, less sensitive to risk than HCs. They also seem to base their decisions on disappointment avoidance less than HCs.

A survival analysis of the last great European plagues: The case of Nonantola (Northern Italy) in 1630.

This paper develops the first survival analysis of a large-scale mortality crisis caused by plague. For the time-to-event analyses we used the Cox proportional hazards regression model. Our case study is the town of Nonantola during the 1630 plague, which was probably the worst to affect Italy since the Black Death. Individual risk of death did not depend on sex, grew with age (peaking at ages 40-60 and then declining), was not affected by socio-economic status, and was positively associated with household size. We discuss these findings in light of the historical-demographic and palaeo-demographic literature on medieval and early modern plagues. Our results are compatible with the debated idea that ancient plague was able to spread directly from human to human. Our methods could be replicated in other studies of European plagues to nuance and integrate the findings of recent palaeo-biological and palaeo-demographic research on plague.

Magnetically enhancing the Seebeck coefficient in ferrofluids.

The influence of the magnetic field on the Seebeck coefficient (Se) was investigated in dilute magnetic nanofluids (ferrofluids) composed of maghemite magnetic nanoparticles dispersed in dimethyl-sulfoxide (DMSO). A 25% increase in the Se value was found when the external magnetic field was applied perpendicularly to the temperature gradient, reminiscent of an increase in the Soret coefficient (S T, concentration gradient) observed in the same fluids. In-depth analysis of experimental data, however, revealed that different mechanisms are responsible for the observed magneto-thermoelectric and -thermodiffusive phenomena. Possible physical and physico-chemical origins leading to the enhancement of the fluids' Seebeck coefficient are discussed.

Effect of the number of removed lymph nodes on prostate cancer recurrence and survival: evidence from an observational study.

BACKGROUND: The aim of this article is to analyze the effect on biochemical recurrence and on overall survival of removing an extensive number of pelvic lymph nodes during prostate cancer surgery. The lack of evidence from randomized clinical trials to address this specific question has hampered the ability to determine the true effect of the number of nodes removed. RESULTS: Our analysis is based on a large observational study, and this can lead unadjusted estimates to be very sensitive to confounding bias due to the different prognosis of individuals. We assess the effect of the number of lymph nodes removed by means of an Inverse Probability Weighting adjustment based on a Poisson regression model, and by a Doubly-robust adjustment. CONCLUSIONS: Our findings suggest that a large number of nodes removed is associated with a significant improvement in time to biochemical recurrence. However, it appears to have no impact on overall survival.

Meta-STEPP with random effects.

We recently developed a method called Meta-STEPP based on the fixed-effects meta-analytic approach to explore treatment effect heterogeneity across a continuous covariate for individual time-to-event data arising from multiple clinical trials. Meta-STEPP forms overlapping subpopulation windows (meta-windows) along a continuous covariate of interest, estimates the overall treatment effect in each meta-window using standard fixed-effects method, plots them against the continuous covariate, and tests for treatment-effect heterogeneity across the range of covariate values. Here, we extend this method using random-effects methods and find it to be more conservative than the fixed-effects method. Both the random- and fixed-effects Meta-STEPP are implemented in R.

Can charged colloidal particles increase the thermoelectric energy conversion efficiency?

Currently, liquid thermocells are receiving increasing attention as an inexpensive alternative to conventional solid-state thermoelectrics for low-grade waste heat recovery applications. Here we present a novel path to increase the Seebeck coefficient of liquid thermoelectric materials using charged colloidal suspensions; namely, ionically stabilized magnetic nanoparticles (ferrofluids) dispersed in aqueous potassium ferro-/ferri-cyanide electrolytes. The dependency of thermoelectric potential on experimental parameters such as nanoparticle concentration and types of solute ions (lithium citrate and tetrabutylammonium citrate) is examined to reveal the relative contributions from the thermogalvanic potential of redox couples and the entropy of transfer of nanoparticles and ions. The results show that under specific ionic conditions, the inclusion of magnetic nanoparticles can lead to an enhancement of the ferrofluid's initial Seebeck coefficient by 15% (at a nanoparticle volume fraction of ∼1%). Based on these observations, some practical directions are given on which ionic and colloidal parameters to adjust for improving the Seebeck coefficients of liquid thermoelectric materials.

Longevity and concentration in survival times: the log-scale-location family of failure time models.

Evidence suggests that the increasing life expectancy levels at birth witnessed over the past centuries are associated with a decreasing concentration of the survival times. The purpose of this work is to study the relationships that exist between longevity and concentration measures for some regression models for the evolution of survival. In particular, we study a family of survival models that can be used to capture the observed trends in longevity and concentration over time. The parametric family of log-scale-location models is shown to allow for modeling different trends of expected value and concentration of survival times. An extension towards mixture models is also described in order to take into account scenarios where a fraction of the population experiences short term survival. Some results are also presented for such framework. The use of both the log-scale-location family and the mixture model is illustrated through an application to period life tables from the Human Mortality Database.

A simple AC calorimeter for specific heat measurement of liquids confined in porous materials: A study of hydrated Vycor.

Nanometric confinement of fluids in porous media is a classical way to stabilize metastable states. Calorimetric studies give insight on the behavior of confined liquids compared to bulk liquids. We have developed and built a simple quasi-adiabatic AC calorimeter for heat capacity measurement of confined liquids in porous media in a temperature range between 150 K and 360 K. Taking the fully hydrated porous medium as a reference, we address the thermal behavior of water as a monolayer on the surface of a porous silica glass (Vycor). For temperature ranging between 160 K and 325 K, this interfacial water shows a surprisingly large heat capacity. We describe the interfacial Hbond network in the framework of a mean field percolation model, to show that at 160 K interfacial water experiences a transformation from low density amorphous ice to a heterogeneous system where transient low and high density water patches coexist. The fraction of each species is controlled by the temperature. We identify the large entropy of the interfacial water molecules as the cause of this behaviour.

Evaluating Markers for Guiding Treatment.

BACKGROUND: The subpopulation treatment effect pattern plot (STEPP) is an appealing method for assessing the clinical impact of a predictive marker on patient outcomes and identifying a promising subgroup for further study. However, its original formulation lacked a decision analytic justification and applied only to a single marker. METHODS: We derive a decision-analytic result that motivates STEPP. We discuss the incorporation of multiple predictive markers into STEPP using risk difference, cadit, and responders-only benefit functions. RESULTS: Applying STEPP to data from a breast cancer treatment trial with multiple markers, we found that none of the three benefit functions identified a promising subgroup for further study. Applying STEPP to hypothetical data from a trial with 100 markers, we found that all three benefit functions identified promising subgroups as evidenced by the large statistically significant treatment effect in these subgroups. CONCLUSIONS: Because the method has desirable decision-analytic properties and yields an informative plot, it is worth applying to randomized trials on the chance there is a large treatment effect in a subgroup determined by the predictive markers.

Competing coexisting phases in 2D water.

The properties of bulk water come from a delicate balance of interactions on length scales encompassing several orders of magnitudes: i) the Hydrogen Bond (HBond) at the molecular scale and ii) the extension of this HBond network up to the macroscopic level. Here, we address the physics of water when the three dimensional extension of the HBond network is frustrated, so that the water molecules are forced to organize in only two dimensions. We account for the large scale fluctuating HBond network by an analytical mean-field percolation model. This approach provides a coherent interpretation of the different events experimentally (calorimetry, neutron, NMR, near and far infra-red spectroscopies) detected in interfacial water at 160, 220 and 250 K. Starting from an amorphous state of water at low temperature, these transitions are respectively interpreted as the onset of creation of transient low density patches of 4-HBonded molecules at 160 K, the percolation of these domains at 220 K and finally the total invasion of the surface by them at 250 K. The source of this surprising behaviour in 2D is the frustration of the natural bulk tetrahedral local geometry and the underlying very significant increase in entropy of the interfacial water molecules.